1. Technical Field
The present disclosure relates to a signal acquisition apparatus and method for distributed compressive sensing and joint signal recovery. More particularly, the present disclosure relates to a signal acquisition apparatus and method for recovering an original signal from compressive sensing signals acquired by a plurality of sensor devices.
The present disclosure was derived from research as a part of big-jump research (or challenge research) support program of the Ministry of Education, Science and Technology [Project management No. NN05850, Project name: coding theoretic compressive sensing for multiple sensor systems].
2. Description of the Related Art
Multiple sensor systems are systems for observing a physical phenomenon that satisfies a specific purpose using a number of sensors distributed in a certain space. For example, multiple sensor systems include a low-power ecological monitoring system, a marine information gathering system, a low-power wireless surveillance and reconnaissance system, and a multi-view video system.
Such a multiple sensor system can acquire more accurate sensing data than that observed by a single sensor, since many sensors observe a phenomenon occurring in a limited space or having a common feature and gather the respectively acquired sensing data.
However, in view of information theory, there is a limit to the total amount of information owned by an arbitrary sensing object. As such, the multiple sensor system gathers the limited information through various sensors, so that redundant information is likely to occur. That is, there is a high correlation between signals sensed by the sensors, and an ‘intra-sensor correlation’ may occur within a signal acquired by each sensor or an ‘inter-sensor correlation’ may occur between the acquired signals.
The successive redundancy of information becomes an important factor that shortens the lifespan of a network. Furthermore, wireless transmission of data having considerable redundancy results in ineffective use of energy accumulated in a battery and causes deterioration in frequency efficiency.
To solve such problems, a joint compressive sensing theory has been proposed. According to the joint compressive sensing theory, compressive sensing signals are subjected to a compression process based on a correlation between signals, whereby limited resources (e.g., energy and a bandwidth) can be efficiently utilized between signals having a high correlation in order to accurately recover an original signal.
However, the conventional joint compressive sensing theory employs the correlation information (inter and intra-sensor correlation information), which is obtained by analyzing signals exchanged between the sensors for a long period of time. Thus, communication between the sensors is needed to obtain the correlation information.
Accordingly, the joint compressive sensing is effective when a distance between sensors is short, but has a problem in that power for transmission/reception is used for signal compression. This acts as significant overhead in light of total power consumption, thereby causing efficiency deterioration.
Moreover, since the respective sensors of the multiple sensor system are basically realized to provide minimum performance, joint compressive sensing is too complicated to be carried out by such small sensors. Accordingly, conventional joint compressive sensing is not a practical alternative due to its low effectiveness.